Large language models (LLMs) have powerful natural language processing capabilities. In this paper, we systematically review the recent literature in this field and highlight the new research opportunities that LLMs bring to text analysis in economics and finance. First, we introduce GPT and BERT, the two most representative LLMs, as well as a number of LLMs developed specifically for economic and financial applications. Additionally, we also elaborate on the fundamental principles behind applying LLMs for text data analysis. Second, we summarize the applications of LLMs in economic and financial text analysis from two perspectives. On the one hand, we highlight the significant advantages of LLMs in traditional text analysis scenarios, such as calculating text similarity, extracting text vectors for prediction, text data identification and classification, building domain-specific dictionaries, topic modeling and analysis, and text sentiment analysis. On the other hand, LLMs have strong human alignment capabilities, thus opening up entirely new application scenarios, i.e., acting as economic agents that simulate humans in generating beliefs or expectations about texts and making economic decisions. Finally, we summarize the limitations and existing research gaps that LLMs face in pioneering new paradigms of economic and financial text analysis research, and discuss potential new research topics that may arise from these issues.

This study investigates the relationship between climate change and systemic risk in China's financial system. First, it examines the responsiveness of systemic risk in the banking, securities, and insurance sectors to extreme climate events, assessing how different financial industries withstand such disasters. The findings confirm that certain extreme climate events can exacerbate systemic financial risk. Second, by constructing a nonlinear autoregressive distributed lag (NARDL) model, this study analyzes the impact of the performance of green and brown market stock indices on the systemic risk of financial sub-sectors. The results indicate that in the short term, an increase in the risk of brown assets and a decrease in their indices significantly amplify systemic risk in the financial industry. However, in the long term, an increase in the brown asset index raises systemic risk in the banking sector, while an increase in the green asset index reduces systemic risk in the securities sector. Furthermore, a reduction in green asset risk significantly lowers systemic risk in the banking sector. In addition, this study underscores the importance of policies addressing the increasing frequency and severity of climate-related disasters. It recommends differentiated financial prudential regulations for green and brown sectors to minimize transition risks associated with climate policy implementation while mitigating physical risks. This approach is crucial to improve risk management frameworks in the financial industry, thereby reducing the impact of both physical and transition risks on systemic risk.

The main purpose of this paper is using the elementary methods, the number of the solutions of some congruence equations and the properties of the classical Gauss sums to study the calculating problem of the fifth power mean of one kind two-term exponential sums, and give the exact calculating formula for it.

With the rising labor costs and increasing resource and environmental constraints in China, coupled with geopolitical conflicts, related industries or production processes are shifting to emerging economies such as Southeast Asia, South Asia, and Mexico. Among these, India's development potential has garnered significant attention, and the "China-to-India industrial relocation model" in the global industrial chain poses a greater impact and threat to China. This paper constructs a pre-quantitative model to measure the impact of industrial relocation on the home country. It designs three scenarios—Ultra-long-term, medium-to-long-term, and short-to-medium-term—And uses counterfactual analysis to assess the impact of India's absorption of China's industrial relocation on China's GDP and employment under different scenarios. The research results indicate that the relocation of industries from China to India will generate significant socio-economic shocks. In the ultra-long-term, this industrial transfer could lead to a 15.6% reduction in China's GDP, a 16.8% decrease in the overall income of the workforce, and a reduction in the number of employed people by 110 million. The impacts are also substantial in the medium-to-long-term and short-to-medium-term scenarios. By sectors, the relocation of low and medium-low R&D intensity manufacturing sectors has a significant impact on the Chinese economy in both the short-to-medium and medium-to-long term perspectives. The relocation of high R&D intensity manufacturing sectors, represented by the computer industry, also causes considerable negative effects on the Chinese economy in the ultra-long-term perspective. This quantitative analysis helps anticipate the economic impact of future changes in industrial layout on China's economy and facilitates the development of preemptive strategies. Based on the medium-to-long-term international economic outlook and the characteristics of domestic regional and industrial economic development, we propose three policy recommendations to provide scientific reference for decision-making by relevant government departments.

The vigorous development of artificial intelligence (AI) is a key initiative to drive technological innovation, achieve industrial upgrading, and enhance the resilience of the Chinese economy. As one of the important applications of AI, industrial robots have transformed the production modes of traditional manufacturing industries by leveraging digital technologies and big data algorithms. Based on data from Chinese listed manufacturing companies on the A-share market between 2012 and 2019, this study explores the impact of industrial robot applications on the resilience of Chinese manufacturing firms. The research findings demonstrate that industrial robot applications significantly enhance firm resilience, which remains robust after a series of robustness tests. Mechanism analysis reveals that industrial robot applications enhance firm resilience through two mechanisms: Improving labor productivity and promoting technological innovation. Heterogeneity analysis indicates that the positive impact of industrial robot applications on firm resilience is more pronounced in non-state-owned enterprises, firms with high technological compatibility, firms with high product technological complexity and regions with higher levels of marketization. This study adds new evidence to the study of the economic consequences of artificial intelligence and expands the literature on the influencing factors of firm resilience. This study also provides theoretical support and policy insights for enhancing firm resilience through artificial intelligence, thereby enhancing the resilience of the Chinese economy.

Existing research has shown that political crisis events can directly impact the tourism industry. However, the current methods suffer from potential changes of unobserved variables, which poses challenges for a reliable evaluation of the political crisis impacts. This paper proposes a panel counterfactual approach with Internet search index, which can quantitatively capture the change of crisis impacts across time and disentangle the effect of the event of interest from the rest. It also provides a tool to examine potential channels through which the crisis may affect tourist outflows. This research empirically applies the framework to analyze the THAAD event on tourist flows from the Chinese Mainland to South Korea. Findings highlight the strong and negative short-term impact of the political crisis on the tourists' intentions to visit a place. This paper provides essential evidence to help decision-makers improve the management of the tourism crisis.

In the digital economy, the emergence of digital currencies has attracted considerable attention from both investors and researchers. However, their high volatility characteristics present new challenges in investment decision-making and risk assessment. To capture the characteristics comprehensively, this paper proposes a novel approach for constructing confidence regions for interval-valued variables based on the exponentially decay weighted bootstrap. The coverage area of the confidence regions and tail quantiles provide new indicators for assessing the volatility and tail risks in the market. Empirical results using Bitcoin as a case study demonstrate the proposed approach outperforms other traditional point-based methods such as exponential weighted moving average in measuring the uncertainty and intraday price volatility. Furthermore, the derived tail quantiles exhibit superior predictive performance for tail risk compared to Value-at-risk methods and the exponential weighted moving average, as evidenced by various tests. The proposed methodology not only contributes a new statistical tool for analyzing digital currency volatility but also provides novel perspectives for extreme risk management in financial markets.

Financial Technology (FinTech) combines financial, inclusive and technological aspects. Under the new development pattern, promoting China's common prosperity cannot be separated from the support of FinTech. Based on the provincial panel data of China from 2011 to 2020, this paper first constructs the common prosperity index from the three dimensions of development, sharing and sustainability, and then examines the impact and function mechanisms of FinTech development on China's common prosperity. The results show that FinTech development can significantly promote China's common prosperity. Further analysis reveals that the coverage of FinTech has a more significant promoting effect on China's common prosperity, and the promotional effect of FinTech development is more obvious on the sustainability of common prosperity, followed by development and the weakest sharing. The results of mechanism analysis show that FinTech development can promote human capital accumulation, enhance marketization, promote the development of the circulation industry, boost residents' consumption, and thus contribute to China's common prosperity by smoothing the domestic circulation. Heterogeneity testing indicates that there exists a regional Matthew effect in FinTech development, but this effect can be mitigated by increasing innovation activities. Therefore, this paper proposes to continuously improve the quality and efficiency of FinTech development, smooth the domestic circulation, strengthen the tilt of digital basic resources, and enhance regional innovation vitality, so as to make FinTech more effective in adding impetus to the realization of China's common prosperity.

Enhancing the welfare of the people is one of the core goals of high-quality development in China's new era. Digital financial inclusion plays a crucial role in improving the subjective well-being of Chinese residents. Utilizing the data from the China Household Finance Survey from 2013 to 2019, and integrating city tiers with municipal digital financial inclusion indices, this paper empirically investigates the impact of digital financial inclusion development on residents' subjective well-being using the ordered Probit model. The findings indicate that the development of digital financial inclusion significantly enhances the subjective well-being of residents. In terms of dimensions, its breadth of coverage and depth of use have a positive impact on residents' well-being, while the degree of digitalization has a negative effect. Moreover, the impact of digital financial inclusion development on subjective well-being varies significantly across different relative income and educational levels. Mechanism analysis shows that the development of digital financial inclusion enhances subjective well-being through three pathways: Improving residents' financial literacy, improving economic conditions, and enhancing social security levels.

This study investigates the impact of two types of FinTech developed and utilized by banks and non-bank financial institutions on fraudulent behavior in China's A-share listed companies. Based on panel data from 2011 to 2020, the research findings suggest that both types of FinTech can suppress corporate fraud by enhancing internal control levels and external monitoring levels. Heterogeneity analysis indicates that the inhibitory effects of both FinTech types are more pronounced in companies with higher levels of digital transformation and lower levels of information disclosure. Additionally, due to differences in operating conditions, strategies, and objectives of FinTech developers, the inhibitory effect of bank FinTech is significant across all firms, whereas the effect of non-bank FinTech is only significant in high-risk firms. When distinguishing types of corporate fraud, both FinTech types significantly inhibit fraudulent activities related to information disclosure, fund utilization, and other categories. Further analysis reveals a complex interaction between the application effects of bank FinTech and non-bank FinTech. Specifically, the inhibitory effect of bank (non-bank) FinTech is significant when the development of other FinTech is high (low). By simultaneously incorporating both types of FinTech and their interaction terms, significant synergistic inhibitory effects are observed in fund misuse and other types of fraud. Finally, the results indicate that the synergistic development of both types of FinTech may introduce potential risks. In summary, this paper, by identifying the impact of FinTech development on corporate fraudulent behaviors, highlights the common characteristics and individual differences of different types of FinTech, emphasizes potential future cooperation opportunities between bank and non-bank FinTech, and points out potential risks in the development of FinTech.

Promoting enterprises to accelerate digital transformation is of great significance to enhance the core competitiveness of enterprises, empower the upgrading of traditional industries, generate new forms of business, as well as drive China's digital economy to become better and stronger. From the perspective of enterprises, this paper analyzes the antecedents of enterprises' digital transformation, constructs related indexes based on the text analysis method, proposes a two-factor theoretical model of manager characteristics and dynamic capabilities, and uses the structural equation model based on partial least squares estimation (PLS-SEM). The empirical results show that manager characteristics such as entrepreneurship, digital evangelist and coordinator, as well as corporate dynamic capabilities such as sensing, learning, integrating and coordinating, have a significantly positive role in promoting the tendency and output of digital transformation of enterprises. In addition, manager characteristics can significantly improve the level of enterprises' dynamic capabilities, and the effect of manager characteristics on enterprises' dynamic capabilities and digital transformation is moderated by managers' perception of policy uncertainty. In addition, the above effects are heterogeneous between state-owned and private enterprises, enterprises in the eastern, central and western regions, as well as enterprises in provincial and non-provincial capitals. This paper fills the research gap on the antecedents of digital transformation, and provide a feasible practical path for enterprises to cultivate managers in the digital era and improve their dynamic capabilities.

Utilizing the 2015, 2017, and 2019 China Household Finance Survey (CHFS) data, combined with the income transition matrix analysis method and empirical analysis method, this study systematically investigates the impact of digital finance on income mobility and income inequality among rural households. The income transition matrix analysis reveals that rural households using digital finance have a higher probability of upward income mobility compared to those not using digital finance. Empirical research has found that digital finance significantly promotes upward income mobility and significantly reduces income inequality among rural households. The mechanism of action indicates that digital finance enhances households' income mobility by improving financial accessibility, facilitating the accumulation of development factors, and promoting off-farm employment opportunities. Furthermore, compared to middle and high-income rural households, digital finance has a greater impact on financial accessibility, development factor accumulation, and off-farm employment for low-income rural households. This consequently reduces income inequality, showcasing the inclusive growth characteristic of digital finance. Further analysis reveals that digital finance primarily impacts rural households' property income and wage income through these three pathways, ultimately promoting overall income mobility and reducing income inequality among households. Both digital payments and digital wealth management significantly contribute to upward income mobility and the reduction of income inequality among rural households, while digital lending has a negligible impact. This study provides empirical evidence to support the enhancement of policies aimed at fostering sustained income growth for rural households and optimizing the rural income distribution pattern through digital finance.

Industrial policy has always played an important role in promoting industrial structure transformation and high-quality economic development. Based on the Five-Year Plan of the province level local governments and the micro-data of Chinese industrial enterprises, this paper constructs a staggered DID identification strategy to empirically analyze the impact of local key industrial policies on firms' TFP. The study found that local key industrial policies can significantly improve the TFP of enterprises through policy effects (financial subsidies, tax breaks, low-interest loans) and competitive effects. Further analysis shows that the way local key industrial policies formulated and implemented will have an important impact on the effect of industrial policies. The impact of local key industrial policies formulated combining with the regional comparative advantage, or implemented dispersedly is better. This study provides Chinese empirical evidence for the impact of industrial policies on firms' productivity, which can provide useful reference for the government to formulate and implement industrial policies and promote high-quality economic development.

With the rise of social media, its impact on the financial transparency of publicly listed companies has received increasing attention. This study investigates how social media, particularly posting activity on East Money's stock message boards, affects the financial fraud behavior of listed companies. Utilizing data from East Money's stock message boards and a bivariate probit regression model, the study finds that the number of posts on the message boards is inversely related to the probability of fraud occurrence and positively related to the probability of fraud detection. This finding indicates that social media may play a dual role in both deterring financial fraud and uncovering it. To address endogeneity issues, the study employs an instrumental variable approach. Additionally, based on the "fraud triangle" theory, the paper proposes and validates two mechanisms through which message board posting activity reduces the likelihood of financial fraud: By decreasing potential opportunities for fraud and increasing the difficulty of rationalizing fraud. Heterogeneity analysis reveals that negative posts and posts by senior users are more effective in curbing financial fraud. This research not only enhances the understanding of how social media can function in corporate governance but also provides insights for regulatory authorities on leveraging social media for financial supervision.

In recent years, China's food delivery industry has undergone substantial growth, driven by the rapid expansion of the platform economy and the influence of the COVID-19 pandemic. Food delivery services have not only lessened customers' sensitivity to delays associated with in-person dining but have also generated increased market demand for merchants. It is noteworthy that the majority of merchants employ a centralized operational mode, which combines food delivery and dine-in services within a single establishment. However, certain merchants opt for a decentralized approach, wherein they establish dedicated food delivery outlets exclusively handling food delivery orders while maintaining an offline restaurant. To examine the impact of food delivery channels on merchant decision-making, this study establishes a dual-channel service system operating within a congestion-prone environment. It characterizes the equilibrium strategy of customers under the two operational policies and investigates how the quality of food delivery services affects merchant profits. Furthermore, the research reveals the optimal operational approach based on varying levels of delivery quality. The key findings of the study are as follows. 1) In the case of decentralized operations, the service capacity allocated to the food delivery channel by the merchant exhibits a non-monotonic relationship with its quality. This implies that higher food delivery quality may gradually prompt the merchant to shift its focus toward the offline channel. 2) Despite the fact that higher food delivery quality has the potential to attract more customers, the study surprisingly finds that improving food delivery quality may actually reduce merchant profits in both centralized and decentralized scenarios. 3) While decentralized operations may lead to decreased order processing efficiency, adopting this approach can effectively mitigate the cannibalization effect of the food delivery channel and result in higher profits, particularly when food delivery quality is high. Consequently, centralized mode is recommended only when the food delivery quality falls within an intermediate range. Additionally, we further validated the robustness of this conclusion from various perspectives, including marginal costs and delivery fees.

"The adjustment of coal-based electric energy transportation" plays a significant role in enhancing the ecological environment and reducing coal consumption in recipient areas. However, it faces challenges in practice. This study examines the "Structure adjustment of coal electric energy transport" from the "Sanxi Region" to the Beijing-Tianjin-Hebei region based on real-world experiences. By employing a multi-regional CGE model, this paper quantitatively analyzes the environmental, economic, and social impacts of this adjustment on both regions. The research reveals that the primary issue with the current transition is the imbalance of interests between the sending and receiving areas. Specifically, while the Beijing-Tianjin-Hebei region benefits from improved air quality, the "Sanxi Region" suffers from negative effects on both the atmosphere and economy. Considering China's 14th Five-Year Plan environmental protection goals, this paper suggests an optimal annual growth range for coal-based electric energy transportation from 2021 to 2025 of [14\%, 27\%]. Additionally, it proposes an optimized tax rate range for joint air pollution control and an economic compensation plan. This research offers a solution path and reference for overcoming challenges in the transformation of coal-based electric energy transportation and contributes to achieving ecological objectives in the Beijing-Tianjin-Hebei region.

We study some properties of Toeplitz operators with positive operator-valued function symbols on the vector-valued exponential weighted Bergman spaces $A^p_{\varphi}(\mathcal{H})\ (1 ＜ p ＜ \infty)$. Firstly, we discuss when the Bergman projection from $L^p_{\varphi}(\mathcal{H})$ onto $A^p_{\varphi}(\mathcal{H})$ is bounded and get the dual of the vector-valued exponential weighted Bergman spaces. Secondly, we obtain several equivalent descriptions of Carleson condition to characterize the boundedness and compactness of Toeplitz operators on $A^p_{\varphi}(\mathcal{H})$. Finally, we consider the Schatten-$p$ class membership of Toeplitz operators acting on $A^2_{\varphi}(\mathcal{H})$.

Let $\mathbb{F}_{q}$ be the finite field of $q$ elements, and $\mathbb{F}_{q^{n}}$ be its extension of degree $n$. An element $\alpha\in \mathbb{F}_{q^{n}}$ is called a normal element of $\mathbb{F}_{q^{n}}/\mathbb{F}_{q}$ if $\{\alpha,\alpha^{q},\ldots, \alpha^{q^{n-1}}\}$ constitutes a basis of $\mathbb{F}_{q^{n}}/\mathbb{F}_{q}$. Normal elements over finite fields have proved very useful for fast arithmetic computations with potential applications to coding theory and to cryptography. The minimal polynomial of a normal element is certainly an irreducible polynomial with nonzero trace, while the converse does not hold in general. Using linearized polynomials, we give some necessary and sufficient conditions for this problem, which extend the known results.

In the context of the development of financial technology, we start with the complex characteristics of financial big data and elaborate on the importance of transfer learning of using multi-source data information to assist target tasks. We explain the significance of transfer learning technology in dealing with data heterogeneity from the perspective of multi-source data, and summarize the relevant concepts and methods of transfer learning technology, including data-driven and model-based transfer learning methods. In addition, this paper proposes the unified framework of transfer learning method based on generalized moment estimation (GMM), gives the effective algorithm of transfer learning, and applies the proposed method to the application of transfer learning in risk value (VaR) and risk measure based on expected quantile (expectile) under multi-source data. Then, we simulate two scenarios where samples are of insufficient or imbalanced sample sizes, respectively, in the application to personal bank credit evaluation, with tests of the prediction accuracy of three transfer learning methods, and analysis of the importance of filtering resource domain information. Finally, we described more application scenarios and development prospects of transfer learning in the financial field.

Green development involves a wide range and covers a large range, so the difference in interest demands makes the government, enterprises and financial institutions unable to reach an effective consensus in the game. This study constructs a tripartite evolutionary game model based on green preference perspective, and analyzes the relationships among green regulation, green transition, and green bond investment. The study found that the green preference of government, enterprises and financial institutions has different effects on green development. Enterprise green preference plays a decisive role in green transformation. Firstly, when the green preference of the enterprise is large, even if the government does not carry out green regulation or financial institutions do not invest in green bonds, the enterprise will still carry out green transformation production. However, when the green preference of other participants is not large enough, the phenomenon that the government makes green regulatory decisions but has no policy effect will occur. Secondly, when the size of the green preference of enterprises is in a specific range, while the green preference of financial institutions and the government is large, there are two possibilities: The simultaneous success or failure of green transition and green bond issuance. Finally, the main conclusions of this paper are verified by numerical simulation.